Data Engineer, ANZSA

The people here at Apple don’t just create products - they create the kind of wonder that’s

revolutionised entire industries. It’s the diversity of those people and their ideas that inspires the

innovation that runs through everything we do, from amazing technology to industry-leading

environmental efforts. Join Apple, and help us leave the world better than we found it.

Apple’s Sales organisation generates the revenue needed to fuel our ongoing development of

products and services. This in turn, enriches the lives of hundreds of millions of people around the world. Our sales team, is in many ways, the face of Apple to our largest customers.

As the Data Engineer, you will be instrumental in the design, development, and implementation of

data engineering solutions for the Apple Channel Sales team in ANZSA (Australia, New Zealand

and South Asia) that have direct and measurable impact on Apple Sales and its customers.

Partnering with internal and external partners you will be responsible for the data that enables our Sales Teams to make informed decisions. You will be the main architect behind our data

infrastructure, ensuring that data is easily accessible, reliable, and actionable. This in turn will

facilitate the automation of sales processes as well as the creation of insights, reports and AI

solutions.

Key responsibilities include building, maintaining and optimising scalable data pipelines for both

structured and unstructured data in production, enabling the development and deployment of

AIML models. Collaborating with various cross functional, regional and global teams, you will

optimise data processes, and ensure that our data practices align with industry standards, best

practices and regulations.

This includes defining and disseminating global best practices, setting technical standards for data architecture, coding and and modelling conventions, and supporting data governance policies to ensure consistent data generation, processing, and reporting. You will safeguard the definitions and integrity of the business metrics our Sales team rely on.

We are looking for an exceptional individual that lives at the intersection of development,

operations, data, and systems engineering to build solutions for scalable data transformation and

delivery.","responsibilities":"Develop and design data integration, pipelines and data ingestion processes for Apple internal

and external data, structured and unstructured data sources.

Handle credentials, secrets and sensitive data securely, applying rigorous secrets management

and data privacy practices across all pipelines and applications.

Architect scalable and efficient data solutions using modern technologies and best practices.

Establish and enforce robust governance of our data assets, driving data scale, quality and

compliance across the organisation.

Implement data hygiene best practices to ensure our data is reliable for actionable insights.

Develop data models and mapping rules to transform raw data into actionable insights and

reports.

Design and implement a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.

Collaborate with internal business stakeholders, cross-functional teams locally, regionally and

globally, external vendors, and partners.

Develop and maintain user documentation, including data models, mapping rules, and data

dictionaries.

Ensure data quality and accuracy by developing data validation and reconciliation processes,

including proactive detection of silent failures and data anomalies before they reach our

stakeholders..

Develop data quality monitoring and validation processes specifically for AIML datasets,

including identifying and addressing data bias.

Understand data requirements for AIML model training and deployment, ensuring data is

available in the appropriate format and quality.

Work with a cross-functional teams to implement data governance, including internal IT teams,

engineers, data analysts, and business operations.

Enforce data governance and compliance strategy with continuous monitoring to assess data

integrity, develop and use KPIs to ensure data quality and inform and align across the

organisation.

Keep up-to-date with the latest industry trends and technologies to ensure work remains

cutting-edge.

Preferred Qualifications

Experience articulating and translating business questions into data solutions and proven ability

to lead development projects from start to finish.

Able to balance competing priorities, long-term projects, and ad hoc requirements.

Demonstrated ability to positively influence and collaborate with people across all functional

areas of an organisation.

Demonstrated ability to achieve strategic goals in an innovative and fast-paced environment.

Outstanding ability to problem solve, develop creative solutions, and demonstrate

resourcefulness, while maintaining extreme attention to detail.

Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and

expansive environment.

Excellent communication skills, able to inspire non-technical colleagues on value proposition and impact of data governance.

Minimum Qualifications

5+ years of experience in designing, building, and maintaining scalable data solutions for large-

scale analytics.

Development experience with cloud database environments like Snowflake, Dremio, Redshift or

Databricks.

Experience deploying and operating data pipelines and services on cloud platforms, including

containerisation, CI/CD, scheduling and production monitoring.

Proficiency in programming languages like SQL, Python, Java or R

Architecting and developing data pipelines through ETL tools, API integrations with systematic

and cloud based source systems.

Strong understanding of data modelling, data warehousing, and ETL concepts

Familiarity with AI/ML model development lifecycle and data needs for training and deployment.

Experience and understanding of unstructured data, API development and basic frontend

development.

Solid understanding of data governance frameworks, secure secrets management, and handling of sensitive data.